Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
By Julius von Kügelgen, Alexander Mey, Marco Loog. 2018
15 citations recorded by the source corpus.
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation and semi-supervised learning. Using ideas from causality, we argue that this requires learning with both causes, X_C, and effects, X_E, of a target variable, Y, and show how this setting leads to what we call a semi-generative model, P(Y,X_E|X_C,θ). Our approach is robust to domain shifts in the distribution of causal features and leverages unlabelled data by learning a direct map from causes to effects. Experiments on synthetic data demonstrate significant improvements in classification over purely-supervised and importance-weighting baselines.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bernhard Schölkopf
- Marco Loog
- Alexander Mey
- Julius von Kügelgen
- Giambattista Parascandolo
- Luigi Gresele
- Alexander Neitz
- Antonio Orvieto
Citations in the corpus
- Learning explanations that are hard to vary (2020, background): “…2012].14 Based on this insight, it was proposed that regression based on causal features should presents desirable invariance and robustness properties [Mooij et al., 2009, Schölkopf et al., 2012, Peters et al., 2016, Rojas-Carulla et al., 2018, Heinze-Deml et al., 2018, von Kügelgen et al., 2018].”
- Semi-supervised learning, causality, and the conditional cluster assumption (2019, methodology): “This type of semi-generative model has been introduced by von Kügelgen, Mey, and Loog [23] in the context of domain adaptation and under the additional assumption of conditionally independent feature sets.”
- Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results (2019, methodology): “Kügelgen et al. (2018) use this knowledge and derive a SSL method which only takes the unlabeled data of effect features into account.”